https://github.com/roushankhalid/datascience-cookbook
This repository is a comprehensive guide to mastering Data Science and Machine Learning, covering Python fundamentals, key libraries (NumPy, Pandas, Matplotlib, Seaborn, Plotly, Scikit-learn), essential ML algorithms, and practical techniques for data visualization, research, and project deployment to solve real-world problems.
https://github.com/roushankhalid/datascience-cookbook
api flask-application jupyter-notebook machine-learning-algorithms matplotlib ml-engineering numpy pandas python scikit-learn seaborn sql sqlserver
Last synced: 11 months ago
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This repository is a comprehensive guide to mastering Data Science and Machine Learning, covering Python fundamentals, key libraries (NumPy, Pandas, Matplotlib, Seaborn, Plotly, Scikit-learn), essential ML algorithms, and practical techniques for data visualization, research, and project deployment to solve real-world problems.
- Host: GitHub
- URL: https://github.com/roushankhalid/datascience-cookbook
- Owner: RoushanKhalid
- Created: 2025-01-28T09:20:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-08T06:25:21.000Z (11 months ago)
- Last Synced: 2025-03-08T06:26:44.041Z (11 months ago)
- Topics: api, flask-application, jupyter-notebook, machine-learning-algorithms, matplotlib, ml-engineering, numpy, pandas, python, scikit-learn, seaborn, sql, sqlserver
- Language: Jupyter Notebook
- Homepage:
- Size: 77.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📊 Data Science, Machine Learning & Research Guide
Welcome to the **Data Science, Machine Learning, and Research Guide** repository! This repository is designed to help you master the core concepts and skills in Data Science (DS) and Machine Learning (ML) while diving into AI/ML-based research projects. Whether you're a beginner or looking to advance your knowledge, this resource provides a comprehensive roadmap for practical learning and contributing to cutting-edge research.
---
## ✨ Features
### 🔍 **Core Concepts**
- **Python for DS & ML**: Learn essential Python libraries like NumPy, Pandas, Matplotlib, and Seaborn.
- **SQL for Data Analysis**: Master SQL commands to manipulate and analyze datasets.
### 🧹 **Data Cleaning & Preprocessing**
- Handle missing data, outliers, and inconsistencies.
- Transform and scale features to prepare datasets for modeling.
### 📊 **Data Analysis & Visualization**
- Discover insights with data visualization tools.
- Analyze trends and patterns with real-world datasets.
### ⚙️ **Feature Engineering**
- Select, create, and transform features to improve model performance.
### 📈 **Basic Statistics for DS**
- Understand key statistical concepts such as:
- Mean, Median, Mode
- Standard Deviation
- Probability Distributions
- Hypothesis Testing
### 🤖 **Model Building**
- Implement algorithms like regression, classification, clustering, and more.
- Tune hyperparameters for optimal results.
### 🚀 **Model Deployment**
- Deploy ML models using Flask or other web frameworks.
- Create interactive web applications for real-world use.
---
## 🔬 **Research Integration**
This repository also aims to foster **AI & ML-based research**, with a focus on:
- **Exploratory Research**: Generate hypotheses and validate them using real-world datasets.
- **Advanced Techniques**: Apply deep learning models, transfer learning, and time-series analysis.
- **Practical Applications**: Develop innovative solutions in fields like robotics, healthcare, finance, and cybersecurity.
- **Literature Reviews**: Summarize and analyze academic papers to gain insights into state-of-the-art methods.
- **Collaboration**: Engage with the community by contributing to or reviewing open-source projects.
---
## 🛣️ Roadmap
Here is the roadmap I follow for structured learning and research:
[👉 AI & ML Learning Roadmap](https://docs.google.com/document/d/19ra-1QJEQRY4giwjMDTNCmXQSaUOBqJHvPm9qrTm5kU/edit?usp=sharing)
---
## 🎯 **Contribute**
You are welcome to contribute your research ideas, code, or insights! Here’s how you can help:
1. Fork this repository and create your feature branch.
2. Submit pull requests with detailed descriptions.
3. Collaborate on open issues or suggest new ones.
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Let’s innovate together and push the boundaries of what AI and ML can achieve! 🚀